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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | SMART TRAFFIC ACCIDENT DETECTION AND AUTOMATED EMERGENCY RESPONSE SYSTEM USING OBJECT DETECTION ALGORITHM |
| Authors Name: | Saravanan P , Karthick B , Kalaiarasu M |
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IJRTI_202052
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| Published Paper Id: | IJRTI2504071 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | This project creates Road accidents remain a major global concern, causing significant loss of life, property damage, and economic setbacks. Timely detection of accidents and accurate classification of vehicle damage can play a crucial role in enhancing emergency response and improving road safety. This research presents an AI-powered Traffic Accident Detection and Damage Classification System that leverages deep learning techniques for real-time monitoring and automated accident reporting. The system utilizes a YOLOv11-based object detection model, trained on a curated dataset from Roboflow, to accurately identify and classify vehicle damage. The proposed framework integrates image processing, intersection unit calculations, and feature classification to detect accident severity with high precision. An automated alert mechanism, including SMS, email, and alarm notifications, ensures immediate emergency response, reducing delays in medical aid and law enforcement intervention. To enhance system reliability, the model undergoes rigorous performance evaluation using key metrics such as accuracy, precision, recall, mean Average Precision (mAP), and confidence scores. The real-time monitoring capability ensures continuous surveillance of roads, capturing accident scenarios as they occur. By implementing an intelligent data preprocessing pipeline, the system improves detection efficiency while minimizing false positives. Unlike traditional accident reporting methods that rely on human intervention, this AI-driven system offers a proactive, automated solution that significantly enhances road safety infrastructure. The findings of this research demonstrate the efficacy and scalability of deep learning-based accident detection, with the potential for integration into smart traffic management systems, connected vehicles, and IoT-based surveillance networks. |
| Keywords: | Accident detection, Artificial intelligence, Deep learning, Real-time monitoring, Traffic safety, YOLO model. |
| Cite Article: | "SMART TRAFFIC ACCIDENT DETECTION AND AUTOMATED EMERGENCY RESPONSE SYSTEM USING OBJECT DETECTION ALGORITHM", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a522-a527, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504071.pdf |
| Downloads: | 000354 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504071
Registration ID:202052
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: a522-a527 Country: coimbatore, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504071 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504071 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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